Papers by Deborah Ferreira
Does My Representation Capture X? Probe-Ably (2021.acl-demo)
Copied to clipboard
| Challenge: | Probing (or diagnostic classification) has become a popular strategy for investigating whether a given set of intermediate features is present in the representations of neural models. |
| Approach: | They propose to use an extendable probing framework to automate the application of probing methods to the user’s inputs. |
| Outcome: | The proposed framework automates the application of probing methods to the user’s inputs. |
Premise Selection in Natural Language Mathematical Texts (2020.acl-main)
Copied to clipboard
| Challenge: | Existing tasks for natural language premise selection are limited and difficult for humans to interpret and write. |
| Approach: | They propose to use natural language premise selection task to predict premises that will be useful to prove a particular statement. |
| Outcome: | The proposed approach improves the performance of baselines and multi-hop premise selection tasks. |
Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference (2022.tacl-1)
Copied to clipboard
| Challenge: | Existing explainable multi-hop inference models are regarded as black-boxes due to their ability to transfer linguistic and semantic information to downstream tasks, posing concerns about interpretability and transparency of their predictions. |
| Approach: | They propose a hybrid framework that integrates explicit constraints with neural architectures through differentiable convex optimization to answer and explain multi-hop questions in natural language. |
| Outcome: | The proposed framework improves performance on scientific and commonsense QA tasks while still providing structured explanations in support of its predictions. |
Natural Language Premise Selection: Finding Supporting Statements for Mathematical Text (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing approaches to understand mathematical discourse are limited by the complexity of word and symbol interactions. |
| Approach: | They propose a task to retrieve supporting definitions and supporting propositions from a dataset that can be used to evaluate different approaches for the task. |
| Outcome: | The proposed task is based on a dataset that can be used to evaluate different approaches for the natural premise selection task. |
To be or not to be an Integer? Encoding Variables for Mathematical Text (2022.findings-acl)
Copied to clipboard
| Challenge: | a number of natural language inference models are limited in interpreting mathematical knowledge written in Natural Language . a variable's meaning is determined exclusively by its defining type, i.e., its context . |
| Approach: | They propose a method that can create context-based representations for variables . they propose 'variable slot' approach which can be used to model variables based on their meaning . |
| Outcome: | The proposed model can be used to represent variables in natural language . it can be applied to a task of variable typing and create context-based representations for variables . |
STAR: Cross-modal [STA]tement [R]epresentation for selecting relevant mathematical premises (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing representations of mathematical statements in natural language are ineffective . STAR model uses cross-modal attention to represent mathematical text . |
| Approach: | They propose a model that uses cross-modal attention to represent mathematical text . it uses conjectures written in both natural and mathematical language to recommend premises . |
| Outcome: | The proposed model outperforms baseline models that do not distinguish between natural and mathematical elements and achieves better performance than state-of-the-art models. |